Robust and Scalable Hyperdimensional Computing With Brain-Like Neural Adaptations

The Internet of Things (IoT) has facilitated many applications utilizing edge-based machine learning (ML) methods to analyze locally collected data. Unfortunately, popular ML algorithms often require intensive computations beyond the capabilities of today's IoT devices. Brain-inspired hyperdimensional computing (HDC) has been introduced to address this issue. However, existing HDCs use static encoders, requiring extremely high dimensionality and hundreds of training iterations to achieve reasonable accuracy. This results in a huge efficiency loss, severely impeding the application of HDCs in IoT systems. We observed that a main cause is that the encoding module of existing HDCs lacks the capability to utilize and adapt to information learned during training. In contrast, neurons in human brains dynamically regenerate all the time and provide more useful functionalities when learning new information. While the goal of HDC is to exploit the high-dimensionality of randomly generated base hypervectors to represent the information as a pattern of neural activity, it remains challenging for existing HDCs to support a similar behavior as brain neural regeneration. In this work, we present dynamic HDC learning frameworks that identify and regenerate undesired dimensions to provide adequate accuracy with significantly lowered dimensionalities, thereby accelerating both the training and inference.Comment: arXiv admin note: substantial text overlap with arXiv:2304.0550

Effect of overweight status at onset on C-peptide levels during first 2 years since diagnosis in children with type 1 diabetes

Background: Recently, the growing epidemic of obesity is mirrored in the increasing incidence rate of T1D in children. However, the role of overweight in the progress of T1D is still unknown. Objective: To assess the relationship between the overweight status at onset and the insulin reserve in the first two years since diagnosis in children with T1D. Methods: One hundred sixty-eight children newly diagnosed with T1D, aged from 1.5 to 18.9 years, with 2-years of follow-up, ≥4 autoantibodies measured at baseline, and onset C-peptide plus 3 or more follow-up measures were included in this study from the Children’s Hospital of Pittsburgh Registry (2004-2006). Baseline demographic and clinical characteristics were compared between overweight and non-overweight subjects. The change and the rate of change of C-peptide were evaluated. The contribution of being overweight to C-peptide levels and change in C-peptide from onset over time were estimated using linear mixed models adjusting for other covariates. Results: Among the 168 subjects with mean age at 9.7 years and mean onset C-peptide of 0.76ng/mL, 22% (36) were overweight at onset with BMI ≥ 85th percentile. Onset C-peptide level of overweight subjects was higher than that of non-overweight (median: 0.88ng/mL vs. 0.50ng/mL, P<0.0001). The highest C-peptide levels (median: 1.86ng/mL vs. 1.47ng/mL, P=0.30) were observed a 3 months, followed by a continuous decline reaching the lowest level at 24 months (median: 0.29ng/mL vs. 0.18ng/mL, P=0.13). Linear mixed models suggest that the overall mean rate of change of the overweight subjects was 0.7865ng/mL/months (95% C.I.: (0.2277, 1.3452), P=0.0062) compared to the non-overweight subjects adjusting for other baseline covariates. The differences of mean C-peptide levels between these two groups decreased as time passed and reached similar levels at the end of the second year. Conclusion: Compared to the non-overweight T1D children, overweight children had higher C-peptide levels at 3, 6, 12, and 18 months after diagnosis; however, at 24 months, this difference was not statistically significant. Public health significance: Children with T1D who are overweight can benefit from the potential related target interventions to help them maintain or extend the duration of high C-peptide level after receiving treatment

The Power of Menus in Contract Design

We study the power of menus of contracts in principal-agent problems with adverse selection (agents can be one of several types) and moral hazard (we cannot observe agent actions directly). For principal-agent problems with $T$ types and $n$ actions, we show that the best menu of contracts can obtain a factor $\Omega(\max(n, \log T))$ more utility for the principal than the best individual contract, partially resolving an open question of Guruganesh et al. (2021). We then turn our attention to randomized menus of linear contracts, where we likewise show that randomized linear menus can be $\Omega(T)$ better than the best single linear contract. As a corollary, we show this implies an analogous gap between deterministic menus of (general) contracts and randomized menus of contracts (as introduced by Castiglioni et al. (2022)).Comment: EC 202

DOMINO: Domain-invariant Hyperdimensional Classification for Multi-Sensor Time Series Data

With the rapid evolution of the Internet of Things, many real-world applications utilize heterogeneously connected sensors to capture time-series information. Edge-based machine learning (ML) methodologies are often employed to analyze locally collected data. However, a fundamental issue across data-driven ML approaches is distribution shift. It occurs when a model is deployed on a data distribution different from what it was trained on, and can substantially degrade model performance. Additionally, increasingly sophisticated deep neural networks (DNNs) have been proposed to capture spatial and temporal dependencies in multi-sensor time series data, requiring intensive computational resources beyond the capacity of today's edge devices. While brain-inspired hyperdimensional computing (HDC) has been introduced as a lightweight solution for edge-based learning, existing HDCs are also vulnerable to the distribution shift challenge. In this paper, we propose DOMINO, a novel HDC learning framework addressing the distribution shift problem in noisy multi-sensor time-series data. DOMINO leverages efficient and parallel matrix operations on high-dimensional space to dynamically identify and filter out domain-variant dimensions. Our evaluation on a wide range of multi-sensor time series classification tasks shows that DOMINO achieves on average 2.04% higher accuracy than state-of-the-art (SOTA) DNN-based domain generalization techniques, and delivers 16.34x faster training and 2.89x faster inference. More importantly, DOMINO performs notably better when learning from partially labeled and highly imbalanced data, providing 10.93x higher robustness against hardware noises than SOTA DNNs

Exploring the relationship between home environmental characteristics and restorative effect through neural activities

As society and the economy have advanced, the focus of architectural and interior environment design has shifted from practicality to eliciting emotional responses, such as stimulating environments and innovative inclusive designs. Of particular interest is the home environment, as it is best suited for achieving restorative effects, leading to a debate between interior qualities and restorative impact. This study explored the relationships between home characteristics, restorative potential, and neural activities using the Neu-VR. The results of the regression analysis revealed statistically significant relationships between interior properties and restorative potential. We examined each potential characteristic of the home environment that could have a restorative impact and elucidated the environmental characteristics that should be emphasized in residential interior design. These findings contribute evidence-based knowledge for designing therapeutic indoor environments. And combining different restorative potential environments with neural activity, discussed new neuro activities which may predict restorativeness, decoded the new indicators of neuro activity for environmental design

Virtual sensing for gearbox condition monitoring based on extreme learning machine

Gearbox, as a critical component to convert speed and torque to maintain machinery normal operation in the industrial processes, has been received and still needs considerable attentions to ensure its reliable operation. Direct sensing and indirect sensing techniques are widely used for gearbox condition monitoring and fault diagnosis, but both have Pros and Cons. To bridge their gaps and enhance the performance of early fault diagnosis, this paper presents a new virtual sensing technique based on extreme learning machine (ELM) for gearbox degradation status estimation. By fusing the features extracted from indirect sensing measurements (e.g. in-process vibration measurement), ELM based virtual sensing model could infer the gearbox condition which was usually directly indicated by the direct sensing measurements (e.g. offline oil debris mass (ODM)). Different state-of-the-art dimension reduction techniques have been investigated for feature selection and fusion including principal component analysis (PCA) and its kernel version, locality preserving projection (LPP) method. The effectiveness of the presented virtual sensing technique is experimentally validated by the sensing measurements from a spiral bevel gear test rig. The experimental results show that the estimated gearbox condition by the virtual sensing model based on ELM and kernel PCA well follows the trend of truth data and presents the better performance over the support vector regression based virtual sensing scheme

RS2G: Data-Driven Scene-Graph Extraction and Embedding for Robust Autonomous Perception and Scenario Understanding

Human drivers naturally reason about interactions between road users to understand and safely navigate through traffic. Thus, developing autonomous vehicles necessitates the ability to mimic such knowledge and model interactions between road users to understand and navigate unpredictable, dynamic environments. However, since real-world scenarios often differ from training datasets, effectively modeling the behavior of various road users in an environment remains a significant research challenge. This reality necessitates models that generalize to a broad range of domains and explicitly model interactions between road users and the environment to improve scenario understanding. Graph learning methods address this problem by modeling interactions using graph representations of scenarios. However, existing methods cannot effectively transfer knowledge gained from the training domain to real-world scenarios. This constraint is caused by the domain-specific rules used for graph extraction that can vary in effectiveness across domains, limiting generalization ability. To address these limitations, we propose RoadScene2Graph (RS2G): a data-driven graph extraction and modeling approach that learns to extract the best graph representation of a road scene for solving autonomous scene understanding tasks. We show that RS2G enables better performance at subjective risk assessment than rule-based graph extraction methods and deep-learning-based models. RS2G also improves generalization and Sim2Real transfer learning, which denotes the ability to transfer knowledge gained from simulation datasets to unseen real-world scenarios. We also present ablation studies showing how RS2G produces a more useful graph representation for downstream classifiers. Finally, we show how RS2G can identify the relative importance of rule-based graph edges and enables intelligent graph sparsity tuning